When General Motors spotted a defect in one of its 2012 Chevrolet Volt vehicles, it quickly went to work mining a database of parts, soon pinpointing the manufacturer of the offending devices and alerting owners. Remarkably, the precision with which the analysis was conducted allowed GM to recall only four vehicles nationwide. Recalls, which can be hugely expensive for manufacturers, are a natural candidate for optimization, and GM said it has used its database for 20 percent of such recalls this year, hoping to find affected cars before they even leave dealers’ lots.

A team of scientists from the University of Manchester used machine learning algorithms to digitally reconstruct an extinct dinosaur and model the way it walked. Their simulation, which used the equivalent of 30,000 desktop computers, determined that the 80 ton beast likely moved at around 5 miles per hour.

Startup Lex Machina uses artificial intelligence algorithms and data analytics to make patent and intellectual property lawyers’ jobs easier. The team has created a large database of patent litigation information, including details on 130,000 cases and 100,000 attorneys, along with subject areas and other details. The database’s creators hope their work can ease the effort and expense of patent litigation.

The City of Fort Lauderdale, Florida partnered with IBM to develop an analytics tool to aid the city’s law enforcement efforts. The system, which pulls data from traffic, transportation, building permits, 911 calls and social media, gives officers access to special dashboards that send alerts identifying high risk areas that merit investigating. In the longer term, the city hopes the tool can be deployed to monitor other aspects of urban development, including neighborhood changes, which could help lawmakers allocate budgets.

The UK-based Open Data Institute, which incubates open data startups and helps government agencies release open data, announced this week that it was expanding globally to include 13 branches around the world. The branches will undertake research and development, provide training and publish data.

Researchers at Columbia University have developed a new data-driven approach to designing nanostructured materials. Their work, which uses machine learning to feed its sophisticated algorithms, can identify previously unobserved chemical structures that may be of future use in a range of fields, from drug design to agriculture. The traditional approach to materials design is iterative and costly, and the creators of the new framework hope their work will help cut costs and reduce the time-to-market of new materials.

Artificial intelligence startup Vicarious announced this week that it had created a program that can crack most CAPTCHAs, online tests that are designed to distinguish computers from humans. The team hopes this demonstration is only a first step in a larger program of mimicking human sensory capabilities. The next step, they stated, could be preparing a meal given arbitrary ingredients.

The United Parcel Service (UPS) has begun deploying a new route guidance system to deliver its packages more quickly and fuel-efficiently. The system, which integrates data from customers, drivers and vehicles, has been in development for nearly 10 years, and promises to reduce unnecessary driving. A reduction of one mile per day for each driver could save the company as much as $50 million per year, a company spokesman said.

Image data from X-rays, MRIs and CT scans present a large and untapped resource for science. One expert says if these images were amassed into a single database, they could be studied to analyze drug efficacy and side effects. In the same way that tissue samples from biopsies are collected and can be analyzed en masse, medical imagery could be turned into a “big data” resource as well.

Douglas Hofstadter, cognitive scientist and early artificial intelligence pioneer, thinks contemporary computer scientists have lost sight of the original goals of his field. An in-depth profile details his break with machine learning, which he says does not offer up enough insights about the nature of cognition, instead merely producing probabilities and predictions. Hofstadter influenced generations of computer scientists with his 1979 book Godel, Escher, Bach: An Eternal Golden Braid, but even some of his most prominent followers, such as Google’s Peter Norvig, argue that the problems he is attempting to solve are simply too difficult at present.